Cooperative attack–defense decision-making of multi-UAV using satisficing decision-enhanced wolf pack search algorithm

被引：2

作者：

Zhou, Tongle ^{[1
,2
]}

Chen, Mou ^{[1
,3
]}

Wang, Yuhui ^{[1
]}

Zhu, Ronggang ^{[3
]}

Yang, Chenguang ^{[4
]}

机构：

[1] College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing

[2] Key Laboratory of Avionics System Integrated Technology, Shanghai

[3] Science and Technology on Electro-optic Control Laboratory, Luoyang Institute of Electro-Optical Equipment of Avic, Luoyang

[4] Bristol Robotics Laboratory, University of the West of England, Bristol

来源：

Soft Computing | 2024年 / 28卷 / 17-18期

基金：

中国国家自然科学基金;

关键词：

Cooperative attack–defense decision-making; Multi-UAV; Satisficing decision; Wolf pack search algorithm;

D O I：

10.1007/s00500-024-09802-z

中图分类号：

学科分类号：

摘要：

Unmanned aerial vehicles (UAVs) have shown their superiority for applications in complicated military missions. A cooperative attack–defense decision-making method based on satisficing decision-enhanced wolf pack search (SDEWPS) algorithm is developed for multi-UAV air combat in this paper. Firstly, the multi-UAV air combat mathematical model is provided and the attack–defense decision-making constraints are defined. Besides the traditional air combat situation, the capability of UAVs and target information including target type and target intention are all considered in this paper to establish the air combat superiority function. Then, the wolf pack search (WPS) algorithm is used to solve the attack decision problem. To improve efficiency, the satisficing decision theory is employed to enhance the WPS to obtain the satisficing solution rather than optimal solution. The simulation results show that the developed method can realize the cooperative attack decision-making. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

引用

页码：9575 / 9586

页数：11

共 32 条

[1]

Zhou T., Chen M., Yang C., Nie Z., Data fusion using Bayesian theory and reinforcement learning method, SCIENCE CHINA Inf Sci, 63, (2020)

[2]

Liu Z., Chen M., Wu Q., Chen S., Prediction of unmanned aerial vehicle target intention under incomplete information, SCIENTIA SINICA Inform, 50, pp. 704-717, (2020)

[3]

Ramirez-Atencia C., Bello-Orgaz G., Moreno M.D., Et al., Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms, Soft Comput, 21, pp. 4883-4990, (2016)

[4]

Gao W., Ding Q., Target allocation in multi-fighter cooperative attacking, Electron Control, 10, 4, pp. 10-14, (2013)

[5]

Cheng C., Wu Q., Liu M., Chen M., Research on task allocation for UCAVs cooperatively attacking multiple targets, J Jilin Univ (Inform Sci Ed), 30, 6, pp. 609-615, (2012)

[6]

Liu S., Tang M., Dong J., Geometric constraint satisfaction using genetic simulated annealing algorithm, J Image Graph, 8, 8, pp. 937-945, (2003)

[7]

Luo D., Wu S., Duan H., Li M., Air-combat decision-making for UAVs cooperatively attacking multiple targets, J Syst Simul, 20, 24, pp. 6778-6782, (2008)

[8]

Liu B., Qin Z., Shao L., Air combat decision making for coordinated multiple target attack using collective intelligence, Acta Aeronaut et Astronat Sin, 30, 9, pp. 1727-1739, (2009)

[9]

Han W., Shen X., Xu J., An efficient adaptive wolf pack search algorithm, Appl Mech Mater, 590, pp. 702-709, (2004)

[10]

Chen Y., Yang D., Yu J., Multi-UAV Task assignment with parameter and time-sensitive uncertainties using modified two-part wolf pack search algorithm, IEEE Trans Aerosp Electron Syst, 54, 6, pp. 2853-2872, (2008)

← 1 2 3 4 →